GLOF Risk Assessment Model in the Himalayas: a Case Study of a Hydropower Project in the Upper Arun River

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Article GLOF Risk Assessment Model in the Himalayas: A Case Study of a Hydropower Project in the Upper Arun River

Rana Muhammad Ali Washakh 1,2 , Ningsheng Chen 1,*, Tao Wang 1, Sundas Almas 3, Sajid Rashid Ahmad 4 and Mahfuzur Rahman 1,2,5

1 Key Laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Key Laboratory for Space Biosciences & Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China 4 College of Earth & Environmental Sciences (CEES), University of the Punjab, Punjab 54590, Pakistan 5 Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka 1230, Bangladesh * Correspondence: [email protected]; Tel.: +86-13808171963

Received: 30 June 2019; Accepted: 30 August 2019; Published: 4 September 2019

Abstract: A glacial lake outburst flood (GLOF) is a phenomenon that is widely known by researchers because such an event can wreak havoc on the natural environment as well as on manmade infrastructure. Therefore, a GLOF risk assessment is necessary, especially within river basins with hydropower plants, and may lead to a tremendous amount of socioeconomic loss if not done. However, due to the subjective and objective limitations of the available GLOF risk assessment methods, we have proposed a new and easily applied method with a wider application and without the need for adaptation changes in accordance with the subject area, which also allows for the repeated use of this model. In this study, we focused our efforts on the Upper Arun Hydroelectric Project (UAHEP) in the Arun River Basin, and we (1) identified 49 glacial lakes with areas greater than 0.1 km2; (2) geographically represented and analyzed these 49 glacial lakes for the period of 1990–2018; (3) analyzed the correlation between the temperature and precipitation trends and the occurrence of recorded GLOF events in the region; (4) proposed a new method based on the documented affected lengths and volumes derived from historical GLOF events to identify 4 potentially critical lakes; and (5) evaluated the discharge profiles using widely used empirical methods and further discussed the physical properties, triggering factors, and outburst probability of the critical lakes. To achieve these objectives, a series of intensive and integrated desk studies, data collections, and GLOF simulations and analyses were performed.

Keywords: GLOF; glacial lakes; trends in discharge; Himalaya; risk assessment

1. Introduction The Himalayas are an abundant resource of water but also have a relatively poor socioeconomic status; thus, this region has been greatly utilized for hydropower projects to help alleviate power loss and poverty. Since 1935, 62 glacial lake outburst flood (GLOF) events initiated from 56 glacial lakes in the Himalayas have been recorded, of which 8 occurred in Nepal [1], and the frequency has reached 1 event every 3–10 years [2–4]. This area has experienced many GLOFs in the past, of which the Cirenmaco GLOF (11 July 1981) located in the Sun Koshi River Basin in China [1,4–7], the Jinco GLOF (27 August 1982) at the headwaters of the Yairuzangbo River of the Pumqu Basin in China [4], the Dig Tsho (4 August 1985) [4,8,9]

Water 2019, 11, 1839; doi:10.3390/w11091839 www.mdpi.com/journal/water Water 2019, 11, 1839 2 of 23 and Tam Pokhari (Sabai-Tsho) GLOFs (3 September 1998) [4,10–13] in the Dudh Basin in Nepal, and the Jialongco GLOF (23 May and 29 June 2002) in the Poiqu Basin in China [14] are good examples of the destructive consequences of GLOF disasters in the Central Himalayas, resulting in the destruction of some of the major hydropower projects, further causing socioeconomic decline [4]. Therefore, before any hydroelectric plant is built in this GLOF-hazard-prone region, a GLOF risk analysis is of crucial importance [15]. As mentioned above, documented GLOF events (such as Lake Cirenmaco and Dig Tsho) in the Central Himalayas destroyed downstream hydropower stations, roads, and bridges; killed hundreds of people; and caused millions of dollars in economic losses [16]. Such GLOFs are hazardous to resident safety, properties, infrastructure (e.g., hydropower, mining, roads, and bridges), agriculture husbandry, pasturelands, forests, tourism, and socioeconomic systems in downstream regions because of their potential to cause catastrophic breaching [4,10,17]. Nepal is endowed with vast water resources, with about 6000 rivers and rivulets contributing to an annual average runoff of 225 billion m3. The total drainage of these watercourses amounts to an area of 194,171 km2, 76% of which falls within Nepal. It is noteworthy that as many as 33 of the larger rivers have drainage areas exceeding 1000 km2. The perennial nature of the rivers and the topography of the country, with steep gradients, provide excellent conditions for hydropower development, the theoretical potential of which has been estimated at 83,000 MW. In reality, however, only 1000 MW (including isolated micro and small hydropower plants)—less than 1.0% of the total potential—has been exploited so far, resulting in only 58% of the total population having access to electricity supply. The present capacity and energy generation is far less than the current electricity demand for both base and peak load and, hence, the country is forced to have load shedding during the dry season. As the electricity demand is projected to grow by 10% per year, the situation will worsen in the years to come if more sources of generation are not added to the system as soon as possible. In this context, the Nepal Electricity Authority (NEA), an undertaking of the Government of Nepal which is responsible for the generation, transmission, and distribution of electricity, has decided to initiate a detailed engineering study on hydropower projects that could be implemented at the earliest possible date. The Upper Arun Hydroelectric Project (UAHEP) is one such attractive project in the Eastern Development Region, which has very high head and firm river flow. The cabinet has also decided to implement the project through the NEA under the ownership of the Government of Nepal. In connection with this, NEA has also envisaged the development of the IkhuwaKhola Hydropower Project (IKHEP) under the umbrella of UAHEP to harness the hydropower potential of the country and to satisfy the increasing domestic power demand. Several methods for assessing the risk of glacial lakes to outburst floods can be found in the literature [16,18–21]. These methods differentiate themselves according to the type of method structure, quantity and range of assessed characteristics, required input data, and percentage of subjectivity in assessment processes [22]. Some of them are regionally focused, and some are adjustable. The demands on input data and the rate of subjectivity of assessment procedures are generally considered as the fundamental obstructions to their repeated use. In [22], the suitability of these methods for use within the Cordillera Blanca was examined. It was shown that none of the applied methods met all of the specified criteria; therefore, a new method is desirable. Once critical lakes are identified, flood modeling and delimitation of endangered areas are the next steps in the risk management procedure [23,24]. The reasons for the presented study are as follows: First, the existing methods are not wholly suitable for use from the perspective of the assessed characteristics and the consideration of regional specificity (especially, the share and representation of various triggers of GLOFs and climate settings [22,25]). Second, the assessment procedures in the majority of these methods are at least partly subjective (based on an expert assessment without giving any thresholds when a clear instructive guide is missing); thus, different observers may reach different results even when the same input data are used. Repeated use is thus considerably limited, and this is considered the fundamental drawback of the present methods as well as a research deficit. Water 2019, 11, 1839 3 of 23

WaterDue 2019 to the, 11, abovementionedx FOR PEER REVIEW reasons, the main objective of this work is to provide a comprehensive3 of 26 and easily repeatable methodological concept for the assessment of the risk of glacial lake hazards within the Due to the abovementioned reasons, the main objective of this work is to provide a comprehensive Arun River Basin, as verified using the data of glacial lakes and GLOFs recorded in this region. The impacts and easily repeatable methodological concept for the assessment of the risk of glacial lake hazards of glacialwithin lake the Arun outburst River floods Basin, cannot as verified ever using be completely the data of eliminated; glacial lakes nevertheless, and GLOFs reliablerecorded assessment in this to identifyregion. The critical impacts glacial of glacial lakes lake is a outburst necessary floods step cannot in understanding ever be completely the effects eliminated; of flood nevertheless, hazards and, consequently,reliable assessment risk management to identify and critical mitigation. glacial lakes Therefore, is a necessary this assessment step in understanding is of great importance. the effects of flood hazards and, consequently, risk management and mitigation. Therefore, this assessment is of Studygreat Area importance. The UAHEP draws water discharged from the Arun River. It is located in the Sankhusabha District Study Area of the Koshi Zone in the Eastern Development Region of Nepal. The proposed dam site is located in a narrowThe gorge UAHEP about draws 350 water m upstream discharged of from the confluencethe Arun River. with It is ChepuwaKhola located in the Sankhusabha in Chepuwa District Village. Theof powerhouse the Koshi Zone lies in in the Hatiya Eastern Village, Development near the Region confluence of Nepal. of The the proposed Arun River dam withsite is LeksuwaKhola. located in a narrow gorge about 350 m upstream of the confluence with ChepuwaKhola in Chepuwa Village. The The project area is situated within longitudes 87 20’00” to 87 30’00”E and latitudes 27 38’24” to powerhouse lies in Hatiya Village, near the conflu◦ence of the Arun◦ River with LeksuwaKhola.◦ The 27◦48’09”N,project area as is presented situated within in Figure longitudes1. The 87°20’00” project to 87°30’00”E area is located and latitudes approximately 27°38’24” to 700 27°48’09”N, km east of Kathmanduas presented and in approximately Figure 1. The project 300 km area north is lo ofcated Biratnagar. approximately Fifty-two 700 hydropowerkm east of Kathmandu project sites and have beenapproximately identified within 300 km the north Koshi of Basin Biratnagar. alone, andFifty-tw theo proposed hydropower UAHEP project is site amongs have the been highest identified priorities of projectswithin the fed Koshi by mountain Basin alone, glaciers, and the which proposed is why UAHE a GLOFP is among risk assessment the highest is priorities one of the of mostprojects important fed partsby of mountain this project. glaciers, which is why a GLOF risk assessment is one of the most important parts of this project.

FigureFigure 1.1. The study study area. area.

In 1985,In 1985, the projectthe project site site for UAHEPfor UAHEP was was recognized recognized during during the the Master Master Plan Plan Study Study of of the the Koshi Koshi River (WaterRiver Resources (Water Resources Development, Development, JICA). InJICA). the summerIn the summer of 1986, of 1986, NEA NEA conducted conducted a reconnaissance a reconnaissance study. In 1991,study. the In Joint 1991, Venture the Joint of Venture Morrison of Morrison Knudsen Knud Corporation,sen Corporation, Lahmeyer Lahmeyer International, International, Tokyo Tokyo Electric PowerElectric Services Power Co., Services and NEPECONCo., and NEPECON carried carried out a feasibility out a feasibility study study of this of this project project on on behalf behalf of of NEA. NEA has given priority to the development of this project to augment the energy generation capability of the integrated Nepal Power System due to its relatively low cost of generation and availability of abundant ﬁrm energy. The feasibility study carried out in 1991 chose an installed capacity of 335 MW Water 2019, 11, 1839 4 of 23 for the peaking run-of-river-type UAHEP. The design discharge of the project was 78.8 m3/sec, and it was expected to generate ﬁrm energy of 2050 GWh per year. In 2008, NEA obtained a license from the Government of Nepal to develop the UAHEP. The updated estimated cost was US$446 million (335 MW/2050 GWh).

2. Materials and Methods A detailed systematic methodological pathway is presented in Figure2. This mainly included collected data in the form of satellite images, details of the historical GLOF events in the form of recorded volumes of the lakes and their corresponding aﬀected lengths in the event of an outburst, and climatic data of the region, which were then analyzed through geographical information systems and statistical and analytical techniques. This methodology resulted in a new GLOF risk assessment model which was then applied to identify potentially critical glacial lakes. Water 2019, 11, x FOR PEER REVIEW 5 of 26

FigureFigure 2.2. Methodology.Methodology.

2.1. Data Collection The data collection included four sets of digital elevation models (DEMs) covering the whole basin and satellite images, namely, 24 series of Landsat 4–5 Thematic Mapper (TM) [26], Landsat 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) from 1990 to 2018 US Geological Survey images, with four images for each year mosaicked to cover the whole basin for the years 1990, 1995, 2000, 2005, 2011, and 2018, resulting in a total of 24 satellite images. Data related to daily, monthly, and annual average temperatures, as well as precipitation for the years 1960–2017, were collected from the China Meteorological Data Service Center (CMDC). We also formed a database of documented volumes and affected disaster lengths of historical GLOF events in the region. Water 2019, 11, 1839 5 of 23

2.1. Data Collection The data collection included four sets of digital elevation models (DEMs) covering the whole basin and satellite images, namely, 24 series of Landsat 4–5 Thematic Mapper (TM) [26], Landsat 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) from 1990 to 2018 US Geological Survey images, with four images for each year mosaicked to cover the whole basin for the years 1990, 1995, 2000, 2005, 2011, and 2018, resulting in a total of 24 satellite images. Data related to daily, monthly, and annual average temperatures, as well as precipitation for the years 1960–2017, were collected from the China Meteorological Data Service Center (CMDC). We also formed a database of documented Watervolumes 2019, 11 and, x FO aﬀRected PEER disasterREVIEW lengths of historical GLOF events in the region. 6 of 26

2.2.2.2. Remote Remote Sensing Sensing AnAn advanced advanced automated automated adaptive lake mapping methodmethod waswas adoptedadopted to to interpret interpret remote remote sensing sensing imagesimages based based on on relevant maps toto discoverdiscover thethe distributiondistribution andand annual annual and and interannual interannual changes changes of of glaciersglaciers and and glacial glacial lakes in the basin.basin. Geometric parametersparameters ofof glacialglacial lakeslakes asas well well as as their their physical physical propertiesproperties and and river course characteristics werewere obtainedobtained throughthrough ﬁeld field investigation. investigation. These These data data were were usedused to to establish establish glacial lake databases and plotplot thethe distributiondistribution mapsmaps ofof glaciers glaciers and and glacial glacial lakes lakes in in thethe research research area. area. TheThe lakes lakes at at risk risk are situated in remote andand inaccessibleinaccessible areas.areas. RemoteRemote sensing sensing provides provides a a feasible feasible method to monitor glacial lakes [17]. An advanced adaptive lake mapping method was used to method to monitor glacial lakes [17]. An advanced adaptive lake mapping method was used to interpret interpret remote sensing images (e.g., TM, Landsat 8, and other high-resolution images) based on remote sensing images (e.g., TM, Landsat 8, and other high-resolution images) based on relevant maps relevant maps so as to determine the distribution, morphological characteristics, and annual and so as to determine the distribution, morphological characteristics, and annual and interannual changes interannual changes of glaciers in the basin. Geometric parameters of glacial lakes, the physical of glaciers in the basin. Geometric parameters of glacial lakes, the physical properties of moraines, properties of moraines, the depth of the lake, parameters of feeding glaciers, and river course the depth of the lake, parameters of feeding glaciers, and river course characteristics were determined characteristics were determined through field investigation and remote sensing analysis, as through ﬁeld investigation and remote sensing analysis, as demonstrated in Figure3. demonstrated in Figure 3.

FigureFigure 3. 3. RemoteRemote sensing sensing using using satellite images to identifyidentify glaciersglaciers andand glacialglacial lakeslakesfor for the the years years ( A(A) ) 1990,1990, ( (BB)) 1995, 1995, ( (CC)) 2000, 2000, ( (DD)) 2005, 2005, ( (EE)) 2011, 2011, and and ( (FF)) 2018. 2018.

In total, we acquired 24 series of Landsat 8 OLI/TIRS images with no or less than 20% cloud In total, we acquired 24 series of Landsat 8 OLI/TIRS images with no or less than 20% cloud cover cover for the periods of November–January in the years 1990, 1995, 2000, 2005, 2011, and 2017. for the periods of November–January in the years 1990, 1995, 2000, 2005, 2011, and 2017. The images The images used in this study were level 1 Geospatial Tagged Image File Format (GeoTIFF) data used in this study were level 1 Geospatial Tagged Image File Format (GeoTIFF) data products, which products, which were preliminarily calibrated. DEM data with a resolution of 90 m from the Shuttle were preliminarily calibrated. DEM data with a resolution of 90 m from the Shuttle Radar Topography Radar Topography Mission (SRTM) were used to obtain topographic information. Mission (SRTM) were used to obtain topographic information. In this study, panchromatic images (band 8) from Landsat 8 OLI were registered with digital topographic maps using Earth Resource Data Analysis System (ERDAS) Imagine software. The registration accuracy was within 15 m (one pixel) in most areas. Furthermore, the images of other bands were resized to 15 m and were registered using the reference information from the panchromatic band. The manual identification of individual glaciers was coupled and supplemented with the Global Land Ice Measurements from Space (GLIMS) database [10,27,28], so that the results could be merged and double-checked [28] to create an updated database. Since an individual dataset from GLIMS, ICIMOD, or CAS was not sufficient, we applied a combination of the three in addition to visual interpretation. A manual interpretation method was used to outline the glaciers and glacial lakes based on false color composite (FCC) images (6, 5, and 3 bands). DEM data were used to determine the dividing line of conjunct glaciers. The accuracy of manual interpretation has been demonstrated as Water 2019, 11, 1839 6 of 23

In this study, panchromatic images (band 8) from Landsat 8 OLI were registered with digital topographic maps using Earth Resource Data Analysis System (ERDAS) Imagine software. The registration accuracy was within 15 m (one pixel) in most areas. Furthermore, the images of other bands were resized to 15 m and were registered using the reference information from the panchromatic band. The manual identiﬁcation of individual glaciers was coupled and supplemented with the Global Land Ice Measurements from Space (GLIMS) database [10,27,28], so that the results could be merged and double-checked [28] to create an updated database. Since an individual dataset from GLIMS, ICIMOD, or CAS was not suﬃcient, we applied a combination of the three in addition to visual interpretation. A manual interpretation method was used to outline the glaciers and glacial lakes based on false color composite (FCC) images (6, 5, and 3 bands). DEM data were used to determine the dividing line of conjunct glaciers. The accuracy of manual interpretation has been demonstrated as optimal for identifying glaciers and glacial lakes because it allows for the consideration of both spectral characteristics and information regarding texture, patterns, shapes, and shadows. Finally, the spatial attributes of glaciers and glacial lakes were calculated using the topological analysis function of ArcInfo software based on the DEM data, and the volumes of glacial lakes were calculated based on their areas.

2.3. Formation of the Model The main objective of this research is to determine the potential critical glacial lakes that could endanger the Hydroelectric Project. According to the results of remote sensing, there are 49 lakes with areas larger than 0.1 km2. In this study, we selected the affected length to determine the potential critical glacial lakes. Firstly, we collected the affected lengths of historical GLOFs that had occurred in the Himalayan region. Secondly, an empirical equation was presented based on the collected data. Thirdly, the presented equation was used to predict the affected lengths of the 49 lakes. The lakes were determined as critical glacial lakes when the predicted affected length was longer than the distance between the lake and the dam (powerhouse) site.

3. Results

3.1. The New Model Data including lake volumes and aﬀected lengths from 11 historical GLOFs in the Himalayan region were collected. The detailed data are listed in Table1.

Table 1. Volume and aﬀected length of historical glacial lake outburst ﬂoods (GLOFs) that occurred in Nepal and Tibet (China).

Volume Aﬀected Length No. Name Date Reference V( 106 m3) L (km) × 1 Cirenmacuo 11 July 1981 18.9 53 [29] 2 Jialongco 23 May 2002 3.9 46 [29] 3 Taaco 28 August 1935 6.3 30 [29] 4 Qiongbixiamacuo 10 July 1940 12.4 44 [29] 5 Sangwang Lake 16 July 1954 300 200 [29] 6 Longdacuo 25 August 1964 10.8 28 [29] 7 Gelhaipuco 21 September 1964 23.4 43 [29] 8 Ayaco 18 August 1970 90 42 [29] 9 Dig Tsho 4 August 1985 8 42 [30] 10 Tam Pokhari 3 September 1988 17.7 66 [31–33] 11 Luggye Tsho 7 October 1994 48 84 [8]

As shown in Figure4, the a ﬀected length increases with the increase in lake volume. The ﬁtting equation can be described as L = 0.52V + 36.13 (1) Water 2019, 11, 1839 7 of 23

Water 2019, 11, x FOR PEER REVIEW 8 of 26 where L is the aﬀected length in km, and V is the lake volume in 106 m3.

FigureFigure 4. 4.Lake Lake volume volume versusversus affected aﬀected length length of of historical historical GLOFs. GLOFs.

3.2.Additionally, Determination the of the upper Critical limit Lakes line can be described as After the formulation of the model, glacial lake databases were established, and the distributions of glaciers and glacial lakes in the researchL =area0.52V were+ mapped.59.21 Furthermore, by carefully examining (2) the elevations and flow paths, the expected outburst paths to the dam site were simulated (Appendix 3.2.A, Determination Figure A2) and of the individual Critical Lakesdistances between glacial lakes and the dam site were calculated in kilometers, as illustrated in Table 2. Table 2 summarizes the glacier inventories in the Arun River Basin After the formulation of the model, glacial lake databases were established, and the distributions for the years 1990–2017, including glacial lake locations with respect to longitudes and latitudes in of glaciers and glacial lakes in the research area were mapped. Furthermore, by carefully examining the decimal degrees followed by glacial lake areas in square kilometers. elevationsThe and sum ﬂow of the paths, lake area the for expected the 49 lakes outburst increased paths from to the23.65 dam to 28.76 site werekm2. The simulated ratio of the (Appendix sum of A, Figurethe A2lake) and area individual in 2017 was distances 1.22 times between that in glacial1990 because lakes and of the the gradual dam site increase were calculated in feeding infrom kilometers, the as illustratedmother glaciers in Table and 2increased. Table2 precipitationsummarizes in the the glacier region. inventories in the Arun River Basin for the years 1990–2017,In this study, including the ratio glacial of a lake lake area locations in 2017 to with that in respect 1990 is to defined longitudes as R. As and shown latitudes in Appendix in decimal degreesA, Figure followed A1, there by glacial are 20 lakes lake areaswith R-values in square larger kilometers. than 1. This indicates that the lake areas of the 20 lakesThe increased sum of the from lake 1990 area to 2017. for the The 49 maximum lakes increased R is 25.23. from There 23.65 are 25 to lakes 28.76 with km2 R-values. The ratio remaining of the sum of theunchanged. lake area There in 2017 are was four 1.22 lakes times with thatR-values in 1990 less because than 1. This of the indicates gradual that increase the lake in areas feeding of these from the four lakes decreased from 1990 to 2017. The minimum R is 0.92. mother glaciers and increased precipitation in the region. Equation (2) was adopted to predict the affected length. The detailed data of lake volume, affected length (Lc), and distance between the lake and the Upper Arun dam site (Ld) are listed in Table 2. As shown in Table 2, the ratios of Lc to Ld of nos. 13, 20, 33, 35, 36, and 42 are larger than 1.0; this indicates that the possible GLOFs of these six lakes may endanger the dam. Water 2019, 11, 1839 8 of 23

Table 2. Annual variation in area, volume, calculated aﬀected length (Lc), and distance between the lake and the dam site (Ld) and the Upper Arun powerhouse (Lp) for the 49 glacial lakes with areas > 0.1 km2 in the Arun River Basin for the period 1990–2017.

Location Lake Area (km2) Volume No. L (km) L (km) Lp (km) Lc/Lp (106 m3) d c Longitude ( ) Latitude ( ) 1990 1995 2000 2005 2011 2017 1 86.305 28.374 3.641 3.727 3.727 3.692 3.762 3.867 212.685 232.190 169.810 248.370 0.680 2 86.379 28.392 0.327 0.410 0.613 0.750 0.903 1.093 60.115 227.220 90.470 243.400 0.370 3 86.415 28.393 0.177 0.177 0.188 0.188 0.181 0.181 9.955 226.140 64.390 242.320 0.270 4 86.494 28.349 0.294 0.294 0.294 0.294 0.294 0.294 16.170 222.570 67.620 238.750 0.280 5 86.582 28.199 1.330 1.330 1.330 1.330 1.287 1.274 70.070 183.600 95.650 199.780 0.480 6 86.629 28.207 0.268 0.268 0.268 0.268 0.268 0.268 14.740 185.890 66.870 202.070 0.330 7 86.863 28.111 0.015 0.098 0.098 0.098 0.312 0.388 21.340 154.050 70.310 170.230 0.410 8 87.028 28.008 0.103 0.103 0.103 0.103 0.103 0.103 5.665 68.360 62.160 84.540 0.740 9 87.047 28.068 0.543 0.574 0.574 0.630 0.707 0.745 40.975 86.570 80.520 102.750 0.780 10 87.051 28.206 0.626 0.626 0.573 0.573 0.573 0.573 31.515 89.910 75.600 106.090 0.710 11 87.082 27.844 0.294 0.294 0.294 0.325 0.340 0.345 18.975 39.570 69.080 39.570 1.750 12 87.082 28.130 0.177 0.177 0.177 0.177 0.177 0.177 9.735 85.520 64.270 101.700 0.630 13 87.101 28.208 0.937 0.937 0.937 0.937 0.937 0.937 51.535 84.230 86.010 100.410 0.860 14 87.105 28.143 0.146 0.146 0.146 0.146 0.146 0.146 8.030 82.330 63.390 98.510 0.640 15 87.112 28.143 0.217 0.217 0.217 0.217 0.217 0.217 11.935 81.610 65.420 97.790 0.670 16 87.134 28.069 0.201 0.201 0.201 0.201 0.201 0.201 11.055 78.140 64.960 94.320 0.690 17 87.428 28.138 0.211 0.211 0.211 0.211 0.211 0.211 11.605 65.420 65.240 81.600 0.800 18 87.443 28.161 0.222 0.222 0.243 0.214 0.239 0.224 12.320 73.010 65.620 89.190 0.740 19 87.468 28.149 0.255 0.255 0.255 0.255 0.255 0.255 14.025 76.260 66.500 92.440 0.720 20 87.472 28.213 1.025 1.025 1.137 1.319 1.319 1.319 72.545 83.280 96.930 99.460 0.970 21 87.480 28.173 0.208 0.208 0.208 0.208 0.208 0.208 11.440 77.290 65.160 93.470 0.700 22 87.502 28.237 0.166 0.166 0.166 0.166 0.166 0.166 9.130 87.290 63.960 103.470 0.620 23 87.563 28.179 0.691 0.803 0.840 0.885 0.989 1.013 55.715 94.610 88.180 110.790 0.800 24 87.578 28.228 0.190 0.190 0.190 0.190 0.190 0.190 10.450 106.750 64.640 122.930 0.530 25 87.578 28.164 0.171 0.180 0.180 0.180 0.180 0.180 9.900 76.350 64.360 92.530 0.700 26 87.584 28.107 0.117 0.117 0.117 0.117 0.117 0.117 6.435 71.840 62.560 88.020 0.710 27 87.587 28.116 0.105 0.105 0.105 0.105 0.105 0.105 5.775 72.190 62.210 88.370 0.700 28 87.591 28.230 0.719 0.786 0.786 0.768 0.786 0.745 40.975 107.770 80.520 123.950 0.650 Water 2019, 11, 1839 9 of 23

Table 2. Cont.

Location Lake Area (km2) Volume No. L (km) L (km) Lp (km) Lc/Lp (106 m3) d c Longitude ( ) Latitude ( ) 1990 1995 2000 2005 2011 2017 29 87.599 28.131 0.122 0.122 0.122 0.122 0.122 0.122 6.710 80.030 62.700 96.210 0.650 30 87.612 28.155 0.127 0.127 0.127 0.127 0.127 0.127 6.985 80.230 62.840 96.410 0.650 31 87.615 28.118 0.241 0.241 0.241 0.241 0.241 0.241 13.255 77.860 66.100 94.040 0.700 32 87.623 28.168 0.200 0.200 0.200 0.200 0.200 0.200 11.000 84.210 64.930 100.390 0.650 33 87.637 28.093 0.377 0.377 0.525 0.564 0.588 0.588 32.340 72.820 76.030 89.000 0.850 34 87.641 28.195 0.497 0.497 0.497 0.497 0.497 0.497 27.335 94.830 73.420 111.010 0.660 35 87.655 28.114 1.289 1.268 1.268 1.268 1.268 1.268 69.740 73.330 95.470 89.510 1.070 36 87.772 27.926 0.636 0.767 0.767 0.794 0.857 0.857 47.135 60.830 83.720 77.010 1.090 37 87.815 27.964 0.175 0.230 0.230 0.342 0.342 0.342 18.810 71.180 68.990 87.360 0.790 38 87.908 27.952 0.663 0.663 0.663 0.632 0.632 0.632 34.760 82.190 77.290 98.370 0.790 39 87.931 27.950 0.597 0.597 0.679 0.745 0.745 0.745 40.975 83.730 80.520 99.910 0.810 40 88.003 27.930 0.853 0.890 0.930 0.930 0.930 0.930 51.150 97.710 85.810 113.890 0.750 41 88.066 27.934 0.728 0.728 0.728 0.728 0.705 0.728 40.040 102.920 80.030 119.100 0.670 42 88.076 27.946 0.593 0.748 0.865 1.108 1.292 1.516 83.380 101.380 102.570 117.560 0.870 43 88.242 28.005 0.264 0.292 0.311 0.323 0.328 0.336 18.480 171.200 68.820 187.380 0.370 44 88.259 28.009 0.351 0.389 0.434 0.514 0.491 0.514 28.270 170.540 73.910 186.720 0.400 45 88.288 28.018 0.390 0.390 0.390 0.390 0.390 0.390 21.450 172.140 70.360 188.320 0.370 46 88.320 28.006 0.331 0.331 0.331 0.331 0.331 0.331 18.205 174.880 68.680 191.060 0.360 47 88.355 28.023 0.479 0.479 0.479 0.479 0.479 0.479 26.345 182.100 72.910 198.280 0.370 48 88.427 28.054 0.857 0.857 0.857 0.857 0.857 0.857 47.135 184.160 83.720 200.340 0.420 49 87.091 27.798 0.504 0.649 0.904 1.003 1.230 1.480 81.400 34.050 101.540 34.050 2.980 Note: Lake nos. 1–10 and 12–48 are located upstream of the dam site; lake nos. 11 and 49 are located downstream of the dam site. Water 2019, 11, 1839 10 of 23

In this study, the ratio of a lake area in 2017 to that in 1990 is defined as R. As shown in AppendixA, Figure A1, there are 20 lakes with R-values larger than 1. This indicates that the lake areas of the 20 lakes increased from 1990 to 2017. The maximum R is 25.23. There are 25 lakes with R-values remaining unchanged. There are four lakes with R-values less than 1. This indicates that the lake areas of these four lakes decreased from 1990 to 2017. The minimum R is 0.92. Equation (2) was adopted to predict the affected length. The detailed data of lake volume, affected length (Lc), and distance between the lake and the Upper Arun dam site (Ld) are listed in Table2. As shown in Table2, the ratios of L c to Ld of nos. 13, 20, 33, 35, 36, and 42 are larger than 1.0; this indicates that the possible GLOFs of these six lakes may endanger the dam. The detailed data of lake volume, affected length (Lc), and distance between the lake and the As shown in Table 2, six glacial lakes were identified as potentially critical glacial lakes (nos. 13, Upper Arun powerhouse (Lp) are listed in Table2. As shown in Table2, the ratios of L c to Lp of nos. 20, 33, 35, 36, and 42) for the Upper Arun dam. We selected three potential critical glacial lakes (nos. 11, 35, 36, and 49 are larger than 1.0; this indicates that the possible GLOFs of these four lakes may 20, 35, and 36). For these three lakes, the values of Lc/Ld are larger than 1.15. These three lakes are the endanger the Upper Arun powerhouse. most critical for the Upper Arun dam. The detailed distribution of these three potential critical glacial As shown in Table2, six glacial lakes were identified as potentially critical glacial lakes (nos. 13, lakes is shown in Figure 5. 20, 33, 35, 36, and 42) for the Upper Arun dam. We selected three potential critical glacial lakes (nos. 20, As shown in Table 2, four glacial lakes were identified as potentially critical glacial lakes (nos. 35, and 36). For these three lakes, the values of Lc/Ld are larger than 1.15. These three lakes are the 11, 35, 36, and 49) for the Upper Arun powerhouse. We selected glacial lake no. 49 as the most critical most critical for the Upper Arun dam. The detailed distribution of these three potential critical glacial lake because the value of Lc/Lp is much larger than those of the other three glacial lakes (nos. 11, 35, lakes is shown in Figure5. and 36) (Table 2).

Figure 5. Locations of glaciers, glacial lakes with areas greater than 0.1 km2, and potentially critical Figure 5. Locations of glaciers, glacial lakes with areas greater than 0.1 km2, and potentially critical glacial lakes (nos. 20, 35, 36, and 49) and their outburst paths. glacial lakes (nos. 20, 35, 36, and 49) and their outburst paths. As shown in Table2, four glacial lakes were identiﬁed as potentially critical glacial lakes (nos. 11, 3.3. Discharge Profiles and Outburst Probability of the Critical Lakes 35, 36, and 49) for the Upper Arun powerhouse. We selected glacial lake no. 49 as the most critical lake The calculated results using empirical formulas listed in Table 3 are shown in Figure 6.

Table 3. Empirical formulas for calculating the peak discharge at the glacial lake dam site.

Formula Source Note = 0.896 QVmw0.0048 [34]

53.0 Qm is the peak discharge at the dam site, and Vw is the = 72.0 VQ [35] m w volume of water. = 66.0 m 045.0 VQ w [36]

Water 2019, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/water Water 2019, 11, 1839 11 of 23

becauseWater 2019 the, 11, valuex FOR PEER of Lc REVIEW/Lp is much larger than those of the other three glacial lakes (nos. 11, 35,2 of and26 36) (Table2). = 017.1 Qm 00077.0 Vw [37] 3.3. Discharge Proﬁles and Outburst Probability of the Critical Lakes

TheThe calculatedpeak discharge results at a using cross empiricalsection was formulas calculated listed using in an Table empirical3 are shown formula in [38]: Figure 6. QV Table 3. Empirical formulas for calculating= the peakmw discharge at the glacial lake dam site. Qxm m LQ (3) FormulaV Source+ Note w Kv 0.896 w Qm = 0.0048Vw [34] 0.53 Qm is the peak discharge at the where Vw is theQ mvolume= 0.72 Vofw water (m3); Qm is the peak[35 discharge] at the dam site (m3/s); Qxm is the peak 0.66 dam site, and Vw is the volume of Q = 0.045V 3 [36] discharge at a mcross sectionw (m /s); L is the distance from the dam (m), and vwater.wK is an empirical = 1.017 coefficient equalQm to0.00077 3.13 Vforw rivers on plains, 7.15 [37for] mountain rivers, and 4.76 for rivers flowing

FigureFigure 6. DischargeDischarge profiles proﬁles of of lake lake nos. nos. 20, 20, 35, 35, 36, 36, and and 39 in 39 ( ina–d (a),– respectively,d), respectively, from from the outburst the outburst site site toto thethe downstream.

TheGLOFs peak caused discharge by the at collapse a cross sectionand erosion was calculatedof moraine using dams an seldom empirical result formula in the drainage [38]: of 100% of the total lake volume. GLOFs caused by avalanche push (seiche) waves also do not normally VwQm mobilize 100% of the lake volume. However,Qxm it= is difficult to predict how much volume will be (3) V + QmL mobilized; therefore, it is necessary to identify and discussw vw Kdifferent scenarios, which include 100%, 75%, and 50% drainage of the lake volume to minimize the maximum and minimum potential risks 3 3 where(FigureV 7,w Tableis the 4). volume of water (m ); Qm is the peak discharge at the dam site (m /s); Qxm is the 3 peak discharge at a cross section (m /s); L is the distance from the dam (m), and vwK is an empirical coefficient equal to 3.13 for rivers on plains, 7.15 for mountain rivers, and 4.76 for rivers flowing through terrain with intermediate relief. GLOFs caused by the collapse and erosion of moraine dams seldom result in the drainage of 100% of the total lake volume. GLOFs caused by avalanche push (seiche) waves also do not normally Water 2019, 11, 1839 12 of 23 mobilize 100% of the lake volume. However, it is difficult to predict how much volume will be mobilized; therefore, it is necessary to identify and discuss different scenarios, which include 100%, 75%, and 50% drainage of the lake volume to minimize the maximum and minimum potential risks (FigureWater7, 2019 Table, 11,4 x). FOR PEER REVIEW 3 of 26

Figure 7. Discharge profiles of lake nos. 20, 35, 36, and 39 with 100%, 75%, and 50% drainage in (a)–(d), Figure 7. Discharge profiles of lake nos. 20, 35, 36, and 39 with 100%, 75%, and 50% drainage in (a)– respectively, from the outburst site to the downstream region. (d), respectively, from the outburst site to the downstream region. As shown in AppendixA, Figure A4, among the identified potential glacial lakes, lake no. 35 Table 4. Maximum discharge and physical features of the critical lakes. seems the most stable considering the change in the area over the past 28 years, while lake no. 49 Discharge at Site if seemsGlacial to be the most unstable in this regard. Physical features of the critical dams are recorded in Drainage = Dam Potential for Dam Outburst TableLake4 above and are visually interpreted in AppendixA, Figure A3. Freeboard Type Lake Impacts Geometry Probability Furthermore,No. 100% previous75% 50% studies have shown that moraine-dammed lakes have higher probabilities of outburst than those of landslide-dammed or erosion lakes [24]. Lake no. 39 (moraine lake, glacier at Landslide Medium 20 4632 3542 2422 Debris flow Stable Medium the end) and no. 49 (moraine lake, glacierdam tongue deep into the lake) are identifiedfreeboard as being moraine dammed and possessing unstable geometry due to the rapid change in theirLow glacial lake area over 35 4929 3772 2583 No dam Debris flow - Medium recent decades. Along with the potential impacts of ice avalanches and rockfreeboard fall because they are closer to the mother glacier, this makes their outburst probabilitiesIce higher compared with the other two lakes. Moraine Low Additionally,36 3951 the volume3025 of2073 lake no. 49 is theavalanches/rock largest among theUnstable four critical lakes, and theHigh higher the dam freeboard volume of a moraine-dammed lake, the higher thefall possibility of outburst [24]. By contrast, lake no. Ice 20 is a landslide-blocking lake, andMoraine glacial lake no. 35 is a glacial erosion lake,Low both of which are not 49 9866* 7586* 5238* avalanches/rock Unstable High easy to break; thus, it can be concludeddam that lake nos. 36 and 49 should befreeboard considered as having high outburst probabilities (Table4). fall * Discharge of lake no. 49 is at the powerhouse site, as it is located downstream of the dam site. As shown in Appendix A, Figure A4, among the identified potential glacial lakes, lake no. 35 seems the most stable considering the change in the area over the past 28 years, while lake no. 49 seems to be the most unstable in this regard. Physical features of the critical dams are recorded in Table 4 above and are visually interpreted in Appendix A, Figure A3. Furthermore, previous studies have shown that moraine-dammed lakes have higher probabilities of outburst than those of landslide-dammed or erosion lakes [24]. Lake no. 39 (moraine lake, glacier at the end) and no. 49 (moraine lake, glacier tongue deep into the lake) are identified as being moraine dammed and possessing unstable geometry due to the rapid change in their glacial

Water 2019, 11, 1839 13 of 23

Table 4. Maximum discharge and physical features of the critical lakes.

Glacial Lake Discharge at Site if Drainage = Potential for Lake Outburst Dam Type Dam Geometry Freeboard Impacts Probability No. 100% 75% 50% Medium 20 4632 3542 2422 Landslide dam Debris ﬂow Stable Medium freeboard 35 4929 3772 2583 No dam Debris ﬂow - Low freeboard Medium 36 3951 3025 2073 Moraine dam Ice avalanches/rock fall Unstable Low freeboard High 49 9866* 7586* 5238* Moraine dam Ice avalanches/rock fall Unstable Low freeboard High * Discharge of lake no. 49 is at the powerhouse site, as it is located downstream of the dam site. Water 2019, 11, 1839 14 of 23

4. Discussion

4.1. Climatic Correlation with GLOFs Data regarding the monthly annual average rainfall for the period of 1960–2017 are represented in Figure8b, which clearly suggests that the monsoon season starts in May and ends in October, reaching the peak monthly average value of over 100 mm in August. Moreover, the rest of the year is dry. There is a rise in monthly annual average temperature from April to July, reaching a peak of about 12 ◦C in June, and then a steady decrease until November. Following a steep fall from October until January, theWater lowest 2019, 11 average, x FOR PEER value REVIEW of 7.5 C is in January. 5 of 26 − ◦

Figure 8. (Figurea) Correlation 8. (a) Correlation among among precipitation, precipitation, temper temperature,ature, and GLOF and events GLOF during events 1935–2017. during (b) 1935–2017. (b) SeasonalSeasonal annual annual average average variation variation inin precipitation during during 1960–2017. 1960–2017. (c) Seasonal (c )annual Seasonal average annual average variation in temperature during 1960–2017. variation in temperature during 1960–2017.

Water 2019, 11, 1839 15 of 23

The seasonal distribution of the precipitation in the project area within Nepal is dominated by a rainy season from May through September, when the monsoons bring 90% of the annual precipitation, and a dry season from November through April. Due to the high elevation and low temperatures in most parts of the Arun River Basin, a certain portion of the precipitation is in the form of snow. No snowfall records are available for the Nepalese portion. Based on records available for the Tibetan region, annual snowfall generally increases with the increment in elevation. At an elevation of 4000 m, about 15%–22% of the annual precipitation falls as snow, and 30%–40% at 4500 m elevation. Snowfall is generally recorded from November through March. There seems to be an increasing trend in the monsoon season (i.e., from May until October), with the peak seasonal annual rainfall of about 80 mm in the years 1970–1975. However, the opposite can be seen in the dry season (i.e., from November to April), with a minimum of no rainfall and a maximum of just below 4 mm. In Figure8c, an overall temperature increase is observed with some minor variations over the past 47 years, with the lowest temperature of 9 C in the seasonal annual average from November to − ◦ April, and a maximum in the range of 8–10 ◦C. The trend line also suggests an increase in the future. In general, Dingri County exhibits a cold climate, though it is expected to take a similar course as the regional temperature rises. As shown in Figure8a, there seems to be an apparent relationship between the increases in temperature and precipitation and the resulting increase in the frequency of glacial lake outbursts. Statistics based on the 27 well-recorded GLOF events show that all of them occurred between May and October (Figure8a), with the majority (18) occurring between June and August. Summarized as an annual cycle, a GLOF can start as early as the beginning of May, for example, the Jialongco event on 23 May 2002, while the latest GLOF may happen in early October, such as the Luggye Tsho event on 7 October 1994. These statistics imply that GLOFs rarely occur during the frozen period from November to March. Rapid warming and precipitation during the ablation season cause ice avalanches and generate massive glacier melt water [5,12], both of which are prone to triggering a GLOF as a result of dam failure. It can be said that in future years, the scale of GLOF events may increase as the annual temperature and precipitation also increase; however, the inﬂuence of climate change on the frequency of GLOFs is very complex. A recent study [39] showed that the average annual frequency of GLOFs had no credible posterior trend, despite reported increases in glacial lake areas in most of the Kush–Karakoram–Himalaya–Nyainqentanglha area. Therefore, the relationship between climatic features and the frequency of GLOF events is still unclear.

4.2. Justification for the Assumed Depth of the Lakes The distances between glacial lakes and the dam (powerhouse) site were obtained from DEMs. It is difficult to obtain the average depth of glacial lakes. A previous study showed that the maximum depth of glacial lakes in the Himalayan region was 55 m. In this study, it was assumed that the depth of the 49 lakes is 55 m [40]. Figure9 shows the relation between the average depth of lakes and the moraine lake area in the Himalayas. The average depth of lakes and the moraine lake area were obtained through field investigation. However, it is appropriate for us to assume that the average depth is 55 m for lakes with areas larger than 0.80 km2. We conducted numerical simulations for four lakes (nos. 20, 35, 36, and 49). The areas of the four lakes each exceed 0.80 km2. Water 2019, 11, x FOR PEER REVIEW 6 of 26

As shown in Figure 8a, there seems to be an apparent relationship between the increases in temperature and precipitation and the resulting increase in the frequency of glacial lake outbursts. Statistics based on the 27 well-recorded GLOF events show that all of them occurred between May and October (Figure 8a), with the majority (18) occurring between June and August. Summarized as an annual cycle, a GLOF can start as early as the beginning of May, for example, the Jialongco event on 23 May 2002, while the latest GLOF may happen in early October, such as the Luggye Tsho event on 7 October 1994. These statistics imply that GLOFs rarely occur during the frozen period from November to March. Rapid warming and precipitation during the ablation season cause ice avalanches and generate massive glacier melt water [5,12], both of which are prone to triggering a GLOF as a result of dam failure. It can be said that in future years, the scale of GLOF events may increase as the annual temperature and precipitation also increase; however, the influence of climate change on the frequency of GLOFs is very complex. A recent study [39] showed that the average annual frequency of GLOFs had no credible posterior trend, despite reported increases in glacial lake areas in most of the Kush–Karakoram–Himalaya–Nyainqentanglha area. Therefore, the relationship between climatic features and the frequency of GLOF events is still unclear.

4.2. Justification for the Assumed Depth of the Lakes The distances between glacial lakes and the dam (powerhouse) site were obtained from DEMs. It is difficult to obtain the average depth of glacial lakes. A previous study showed that the maximum depth of glacial lakes in the Himalayan region was 55 m. In this study, it was assumed that the depth of the 49 lakes is 55 m [40]. Figure 9 shows the relation between the average depth of lakes and the moraine lake area in the Himalayas. The average depth of lakes and the moraine lake area were obtained through field investigation. However, it is appropriate for us to assume that the average depth is 55 m for lakes with areas larger than 0.80 km2. We conducted numerical simulations for four lakesWater 2019(nos., 11 20,, 1839 35, 36, and 49). The areas of the four lakes each exceed 0.80 km2. 16 of 23

Figure 9. Relationship between moraine lake area and average depth. Figure 9. Relationship between moraine lake area and average depth. 4.3. Analysis, Limitations, and Recommendations in Relation to the Model

The model is based on the influenced lengths of 11 historical GLOF events, and the reason for choosing the upper limit line (Equation (2)) is that it covers more data than the best-fit line. The findings of our study identify lake nos. 20, 35, 36, and 49 as the most critical due to their location, volume, physical features, and distance from both the Upper Arun dam and powerhouse. In other words, the values of Lc/Ld or Lc/Lp are large enough to pose serious risks as compared to all the other glacial lakes. Although lake no. 11 is in close proximity to lake no. 49, its lesser volume and more stable dam geometry (and thus, lower potential discharge) make it nonthreatening in the case of an outburst. This model is not only limited to the Himalayas but can also be applied to other mountainous regions. The results of this study investigating and analyzing the influence of GLOFs on the Upper Arun Hydroelectric Project and IkhuwaKhola Hydropower Project were peer-reviewed and accepted by a joint venture (CSPDR-SINOTECH JV) led by the Changjiang Institute of Survey, Planning, Design, and Research (CSPDR) with the NEA for the project entitled “Detailed Engineering Design and Preparation of Bidding Documents for Construction of Upper Arun Hydroelectric Project and IkhuwaKhola Hydropower Project”. The number of historical GLOF events used to devise the model is limited, while a larger dataset could strengthen the numerical relationship between the volume and the affected length of a GLOF. The reason for the restricted set of data used to derivate the model is that the influenced lengths of most past GLOFs were not recorded. Moreover, a quasi-process such as an earthquake can trigger a GLOF hazard, regardless of the volume and distance of the glacial lake from the site of the assessed project. Another factor that needs to be addressed is the fact that a chain outburst of glacial lakes can happen, but prediction of such an event requires further study. Water 2019, 11, 1839 17 of 23

The field visit by our research team in April 2018 (AppendixA, Figures A5–A8) concluded that the topography and characteristics of the river channel are fairly consistent over the studied site [41]. Thus, these two factors were eliminated in the proposed model to minimize the uncertainties in the results. Although it would be very challenging, in future studies, characteristics of the river channel and an integrated model may be proposed incorporating more factors. It may also be possible to explore the possible destruction-triggering mechanism for each glacial lake in addition to the four critical lakes analyzed in Section 3.3. However, it would be very difficult to discuss the failure of a glacial lake dam under different triggering factors. It is important to address the factors that affect GLOF fluctuation, such as the outburst dynamic process and the scale. Nevertheless, to accomplish this task, the most basic step is to collect samples—which is not easy, as the terrain and environment at the glacial lakes’ sites make it impossible to physically travel there—and to test the particle size and mechanical parameters of the samples.

5. Conclusions Extensive remote sensing identified 49 lakes with areas larger than 0.1 km2, namely, lake nos. 1–10 and 12–48, which are located upstream of the dam site, and lake nos. 11 and 49, which are located downstream of the dam site. The correlation between climatic features and the occurrence of GLOF events needs further investigation to conclude if there is a significant relationship or not, but it can be said that the scale of GLOF events may increase over the coming years as the trends of temperature and precipitation in the region are predicted to increase in the future. In this study, a new but effective method was used to select the affected disaster lengths to determine the potentially critical glacial lakes. First, the affected disaster lengths of historical GLOFs that occurred in the Himalayan region were collected. Second, an empirical equation was determined based on the collected data. Third, the determined equation was used to predict the affected lengths of those 49 lakes. The lakes were considered as potentially critical glacial lakes when the predicted affected length was larger than the distance between the lake and the dam (powerhouse) site. The reason for selecting four potentially critical glacial lakes (nos. 20, 35, 36, and 49) to conduct further investigation is that the values of Lc/Ld or Lc/Lp for these four lakes are larger compared with those of the other lakes. In other words, these four glacial lakes are potentially more critical. Further analysis to characterize the probability of an outburst was done based on the physical features and triggering factors of the lakes. In the case of an outburst event at lake no. 49, which is located downstream of the dam, flow-diverging engineering could be considered for preventing powerhouse destruction.

Author Contributions: Conceptualization, R.M.A.W. and N.C.; methodology, R.M.A.W., T.W., S.R.A., and N.C.; software, R.M.A.W.; validation, S.A. and S.R.A.; formal analysis, R.M.A.W., T.W., and N.C.; investigation, R.M.A.W., T.W., and N.C.; resources, N.C.; data curation, R.M.A.W., S.A., and M.R.; writing—original draft preparation, R.M.A.W., T.W., and N.C.; writing—review and editing, R.M.A.W., S.A., and S.R.A.; visualization, R.M.A.W., N.C., and T.W.; supervision, N.C.; project administration, N.C.; funding acquisition, N.C. Funding: The National Natural Science Foundation of China supported this study (Grant Nos. 41861134008, 41771045 and 41671112). Acknowledgments: We are grateful to the “Center for Digital Mountain and Remote Sensing Application, IMHE, Chengdu” for providing satellite images. Conﬂicts of Interest: The authors declare no conﬂict of interest. Water 2019, 11, x FOR PEER REVIEW 8 of 26

triggering factors of the lakes. In the case of an outburst event at lake no. 49, which is located downstream of the dam, flow-diverging engineering could be considered for preventing powerhouse destruction.

Funding: The National Natural Science Foundation of China supported this study (Grant Nos. 41861134008, 41771045 and 41671112).

Acknowledgments: We are grateful to the “Center for Digital Mountain and Remote Sensing Application, IMHE, Chengdu” for providing satellite images.

Conflicts of Interest: The authors declare no conflict of interest. Water 2019, 11, 1839 18 of 23 Appendix A Appendix A

Water 2019, 11, x FOR PEER REVIEW 9 of 26 Figure A1. The ratio of lake area in 2017 to that in 1990 for each lake. Figure A1. The ratio of lake area in 2017 to that in 1990 for each lake.

Figure A2. Simulated outburst paths. Figure A2. Simulated outburst paths.

Water 2019, 11, 1839 19 of 23 Water 2019, 11, x FOR PEER REVIEW 10 of 26

FigureFigure A3. WaterGlacialA3. Glacial2019, 11 lake, x lakeFOR change PEER change REVIEW in in area area proﬁle profile duringduring the the period period 2005–2017, 2005–2017, where where (a), (ba11), ( ofcb ),26), and (c), ( andd) (d) represent lake nos. 20, 35, 36, and 49, respectively (Source: Google Earth). represent lake nos. 20, 35, 36, and 49, respectively (Source: Google Earth).

Figure A4. CriticalFigure A4. change Critical change in area in area proﬁle profile ofof glacial glacial lakes lakes during during the period the 1990–2018. period 1990–2018.

Figure A5. Field investigation route from Kathmandu to Barun Khola.

Water 2019, 11, x FOR PEER REVIEW 11 of 26

Figure A4. Critical change in area profile of glacial lakes during the period 1990–2018. Water 2019, 11, 1839 20 of 23

Water 2019, 11, x FOR PEER REVIEW 12 of 26 Figure A5. Field investigation route from Kathmandu to Barun Khola.

Figure A5. Field investigation route from Kathmandu to Barun Khola.

Figure A6. Field investigation route from Barun Khola to lower dam site.

Figure A7. Interview with the locals.

Water 2019, 11, x FOR PEER REVIEW 12 of 26

Water 2019, 11, 1839 21 of 23 Figure A6. Field investigation route from Barun Khola to lower dam site.

Water 2019, 11, x FOR PEER REVIEW Figure A7. Interview with the locals. 13 of 26 Figure A7. Interview with the locals.

Figure A8. Field activities at the lower dam site.

References Figure A8. Field activities at the lower dam site.

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